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Verwer, K.: Freiheit und Verantwortung bei Hans Jonas (2011) 0.08

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Content: Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.

Roth, G.; Strüber, N.: Wie das Gehirn die Seele macht (2018) 0.07

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Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.07

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Source: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

Erpenbeck, J.; Sauter, W.: Stoppt die Kompetenzkatastrophe! : Wege in eine neue Bildungswelt (2019) 0.07

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Unzicker, A.: Vom Urknall zum Durchknall (2010) 0.06

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Liessmann, K.P.: Geisterstunde : die Praxis der Unbildung ; eine Streitschrift (2014) 0.06

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Costandi, M.: Neuroplasticity (2016) 0.06

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Roth, G.; Strüber, N.: Wie das Gehirn die Seele macht (2014) 0.05

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Sweller, J.; Ayres, P.; Kalyuga, S.: Cognitive load theory (2011) 0.05

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Cognitive load theory (2010) 0.05

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Lippmann, W.: ¬Die öffentliche Meinung : wie sie entsteht und manipuliert wird (2018) 0.05

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O'Connor, C.; Weatherall, J.O.: ¬The misinformation age : how false ideas spread (2019) 0.05

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Blanco, E.; Cankaya, H.C.; Moldovan, D.: Composition of semantic relations : model and applications (2010) 0.05

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Content: Vgl. unter: http://www.hlt.utdallas.edu/~eduardo/papers/csr_model_and_applications.pdf. Vgl. für den Proceedingsband: http://www.aclweb.org/anthology/C/C10/C10-1000.pdf.

Gödert, W.; Lepsky, K.: Informationelle Kompetenz : ein humanistischer Entwurf (2019) 0.05

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Footnote: Rez. in: Philosophisch-ethische Rezensionen vom 09.11.2019 (Jürgen Czogalla), Unter: https://philosophisch-ethische-rezensionen.de/rezension/Goedert1.html. In: B.I.T. online 23(2020) H.3, S.345-347 (W. Sühl-Strohmenger) [Unter: https%3A%2F%2Fwww.b-i-t-online.de%2Fheft%2F2020-03-rezensionen.pdf&usg=AOvVaw0iY3f_zNcvEjeZ6inHVnOK]. In: Open Password Nr. 805 vom 14.08.2020 (H.-C. Hobohm) [Unter: https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzE0MywiOGI3NjZkZmNkZjQ1IiwwLDAsMTMxLDFd].

Wolfangel, E.: ¬Die Grenzen der künstlichen Intelligenz (2016) 0.04
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Content

"Wie lernen Maschinen überhaupt? Eine zentrale Unterscheidung betrifft die Art des Lernens: Algorithmen können überwacht oder unüberwacht lernen. Ersteres wird unter anderem für Klassifikationsaufgaben genutzt: Ist beispielsweise ein Mensch auf einem Foto oder nicht? Grundlage dafür sind Trainingsdaten, anhand derer der Algorithmus auf Vorgabe eines Menschen lernt, was das richtige Ergebnis ist - auf diesen 1000 Bildern ist ein Mensch, auf diesen 1000 nicht. Hat das System für alle eventuell vorkommenden Fälle genügend Trainingsdaten, so die Idee, lernt es daraus selbst, bislang unbekannte Bilder zu klassifizieren. Alphago lernte beispielsweise unter anderem anhand von Millionen menschlicher Go-Spielzüge.
Neuronale Netze lernen aus Erfahrungen Angesichts immer größerer Computer und wachsender Masse an Trainingsdaten gewinnen bei der Bilderkennung so genannte neuronale Netze immer mehr an Bedeutung. "Sie sind heute die leistungsfähigsten Mustererkennungsverfahren", sagt Hamprecht. Dabei wird die Funktionsweise des menschlichen Gehirns lose nachgeahmt: Die Netze bestehen aus mehreren Lagen mit einer festzulegenden Anzahl an Neuronen, deren Verbindungen sich verstärken oder abschwächen abhängig von den "Erfahrungen", die sie machen. Solche Erfahrungen sind beispielsweise die Trainingsdaten aus dem überwachten Lernen und das Feedback, ob zu einem Trainingsdatum die richtige oder falsche Vorhersage gemacht wurde. Dank der vielen Übungsdaten lassen sich heute sehr viel größere und tiefere Netze trainieren als noch vor einigen Jahren. Während früher ein berühmter Computer-Vision-Datensatz aus 256 Bildern und sein Nachfolger aus 1000 Bildern bestand, gibt es heute Datensätze mit einer Million gelabelter Bilder - also solche, auf denen Menschen markiert haben, was darauf zu sehen ist. Aber die Netze haben auch entscheidende Haken: "Man kann bei neuronalen Netzen schwer nachvollziehen, wie sie zu einer Entscheidung kamen", sagt Hamprecht. Zudem beruhe der Entwurf neuronaler Netze auf einer großen Willkür: Bei der Entscheidung, wie viele Lagen mit wie vielen Neuronen genutzt werden sollten, beruhe vieles auf Bauchgefühl oder auf Ausprobieren. Die Entwickler testen verschiedene Varianten und schauen, wann das beste Ergebnis entsteht. Erklären können sie ihre Entscheidung nicht. "Dieses Rumprobieren gefällt mir nicht", sagt Hamprecht, "ich gehe den Dingen lieber auf den Grund.""
Lorenz, D.: Occupy Publishing! : Wie veröffentlichen wir in Zukunft? (2012) 0.04
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Abstract

"Über 1000 Mathematikerinnen und Mathematiker aus aller Welt erklären öffentlich ihren Boykott des Elsevier-Verlages auf der Webseite http://thecostofknowledge.com, und unter dem gleichen Namen veröffentlichen 34 namhafte Mathematiker einen offenen Brief, in dem sie in klarer Sprache den Verlag kritisieren (siehe auch die deutsche Übersetzung des offenen Briefes ab Seite 16 dieses Heftes): "What all the signatories do agree on is that Elsevier is an exemplar of everything that is wrong with the current system of commercial publication of mathematics journals, and we will no longer acquiesce to Elsevier's harvesting of the value of our and our colleagues' work." Wie konnte es dazu kommen? Die Geschichte beginnt wahrscheinlich schon dent Ende der 90er Jahre von Rob Kirby, doch mit Hilfe des Web 2.0 hat vor langer Zeit, zuminmit einem offenen Brief sie in den vergangenen Monaten erstaunlich an Fahrt gewonnen. Der Beitrag bietet eine kurze Chronologie der Ereignisse."

Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.04

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Content: Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

Suchenwirth, L.: Sacherschliessung in Zeiten von Corona : neue Herausforderungen und Chancen (2019) 0.04

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Footnote: https%3A%2F%2Fjournals.univie.ac.at%2Findex.php%2Fvoebm%2Farticle%2Fdownload%2F5332%2F5271%2F&usg=AOvVaw2yQdFGHlmOwVls7ANCpTii.

Kann das Gehirn das Gehirn verstehen? : Gespräche über Hirnforschung und die Grenzen unserer Erkenntnis mit Wolf Singer, Gerald Hüther, Gerhard Roth, Angela D. Friederici, Henning Scheich, Hans J. Markowitsch, Christoph von der Malsburg, Randolf Menzel, Frank Rösler (2013) 0.04

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Vivanco, L.; Bartolomé, B.; San Martín, M.; Martínez, A.: Bibliometric analysis of the use of the term preembryo in scientific literature (2011) 0.03
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Abstract

Our objective was to determine the prevalence of the term preembryo in the scientific literature using a bibliometric study in the Web of Science database. We retrieved data from the Web of Science from 1986 to 2005, covering a range of 20 years since the term was first published. Searches for the terms embryo, blastocyst, preimplantation embryo, and preembryo were performed. Then, Boolean operators were applied to measure associations between terms. Finally, statistical assessments were made to compare the use of each term in the scientific literature, and in specific areas where preembryo is most used. From a total of 93,019 registers, 90,888 corresponded to embryo; 8,366 to blastocyst; 2,397 to preimplantation embryo; and 172 to preembryo. The use frequency for preembryo was 2:1000. The term preembryo showed a lower cumulative impact factor (343) in comparison with the others (25,448; 5,530; and 546; respectively) in the highest scored journal category. We conclude that the term preembryo is not used in the scientific community, probably because it is confusing or inadequate. The authors suggest that its use in the scientific literature should be avoided in future publications. The bibliometric analysis confirms this statement. While preembryo hardly ever is used, terms such as preimplantation embryo and blastocyst have gained wide acceptance in publications from the same areas of study.

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